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Showing 8 results for Bootstrap
Nasrollah Iranpanah,
Volume 3, Issue 2 (3-2010)
Abstract
Abstract: In many environmental studies, the collected data are usually spatially dependent. Determination of the spatial correlation structure of the data and prediction are two important problem in statistical analysis of spatial data. To do so, often, a parametric variogram model is fitted to the empirical variogram of the data by estimating the unknown parameters of the mentioned variogram. Since there are no closed formulas for the variogram parameters estimator, they are usually computed numerically. Therefore, the precision measures of the variogram parameters estimator and spatial prediction can be calculated using bootstrap methods. Lahiri (2003) proposed the moving block bootstrap method for spatial data, in which observations are divided into several moving blocks and resampling is done from them. Since, in this method, the presence of boundary observations in the resampling blocks have less selection chance than the other observations, therefore, the estimator of the precision measures would be biased. In this paper, revising the moving block bootstrap method, the separate block bootstrap method was presented for estimating the precision measures of the variogram parameters estimator and spatial prediction. Then its usage was illustrated in an applied example.
Nahid Sanjari Farsipour, Hajar Riyahi,
Volume 7, Issue 2 (3-2014)
Abstract
In this paper the likelihood and Bayesian inference of the stress-strength reliability are considered based on record values from proportional and proportional reversed hazard rate models. Then inference of the stress-strength reliability based on lower record values from some generalized distributions are also considered. Next the likelihood and Bayesian inference of the stress-strength model based on upper record values from Gompertz, Burr type XII, Lomax and Weibull distributions are considered. The ML estimators and their properties are studied. Likelihood-based confidence intervals, exact, as well as the Bayesian credible sets and bootstrap interval for the stress-strength reliability in all distributions are obtained. Simulation studies are conducted to investigate and compare the performance of the intervals.
Ehsan Kharati Koopaei, Soltan Mohammad Sadooghi Alvandi,
Volume 8, Issue 1 (9-2014)
Abstract
The coefficient of variation is often used for comparing the dispersions of populations that have different measurement systems. In this study, the problem of testing the equality of coefficients of variation of several Normal populations is considered and a new test procedure based on Wald test and parametric bootstrap approach is developed. Since all the proposed tests for this problem are approximate, it is important to investigate how well each test controls the type I error rate. Therefore, via a simulation study, first the type I error rate of our new test is compared with some recently proposed tests. Then, the power of our proposed test is compared with others.
Sana Eftekhar, Ehsan Kharati-Koopaei, Soltan Mohammad Sadooghi-Alvandi,
Volume 9, Issue 2 (2-2016)
Abstract
Process capability indices are widely used in various industries as a statistical measure to assess how well a process meets a predetermined level of production tolerance. In this paper, we propose new confidence intervals for the ratio and difference of two Cpmk indices, based on the asymptotic and parametric bootstrap approaches. We compare the performance of our proposed methods with generalized confidence intervals in term of coverage probability and average length via a simulation study. Our simulation results show the merits of our proposed methods.
Atefe Pourkazemi, Hadi Alizadeh Noughabi, Sara Jomhoori,
Volume 13, Issue 2 (2-2020)
Abstract
In this paper, the Bootstrap and Jackknife methods are stated and using these methods, entropy is estimated. Then the estimators based on Bootstrap and Jackknife are investigated in terms of bias and RMSE using simulation. The proposed estimators are compared with other entropy estimators by Monte Carlo simulation. Results show that the entropy estimators based on Bootstrap and Jackknife have a good performance as compared to the other estimators. Next, some tests of normality based on the proposed estimators are introduced and the power of these tests are compared with other tests.
Marjan Rajabi,
Volume 14, Issue 1 (8-2020)
Abstract
The advent of new technology in recent years has facilitated the production of high dimension data. In these data we need evaluating more than one assumption. Multiple testing can be used for the collection of assumptions that are simultaneously tested and controlled the rate of family wise error that is the most critical issue in such tests. In this report, the authors apply Sidak and Stepwise strategies for controlling family wise error rate in detecting outlier profiles and comparing to each other. Considering our simulation results, the performance of such methods are compared using the parametric bootstrap snd by applying on real data in dataset illustrate the implementation of the proposed methods.
Ahad Malekzadeh, Asghar Esmaeli-Ayan, Seyed Mahdi Mahmodi,
Volume 15, Issue 1 (9-2021)
Abstract
The panel data model is used in many areas, such as economics, social sciences, medicine, and epidemiology. In recent decades, inference on regression coefficients has been developed in panel data models. In this paper, methods are introduced to test the equality models of the panel model among the groups in the data set. First, we present a random quantity that we estimate its distribution by two methods of approximation and parametric bootstrap. We also introduce a pivotal quantity for performing this hypothesis test. In a simulation study, we compare our proposed approaches with an available method based on the type I error and test power. We also apply our method to gasoline panel data as a real data set.
Dr Adeleh Fallah,
Volume 19, Issue 1 (9-2025)
Abstract
In this paper, estimation for the modified Lindley distribution parameter is studied based on progressive Type II censored data. Maximum likelihood estimation, Pivotal estimation, and Bayesian estimation were calculated using the Lindley approximation and Markov chain Monte Carlo methods. Asymptotic, Pivotal, bootstrap, and Bayesian confidence intervals are provided. A Monte Carlo simulation study has been conducted to evaluate and compare the performance of different estimation methods. To further illustrate the introduced estimation methods, two real examples are provided.
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